Generalized information filtering for MAV parameter estimation

In this paper we present a new estimation algorithm that allows for the combination of information from any number of process and measurement models. This adds more flexibility to the design of the estimator and in our case avoids the need for state augmentation. We achieve this by adapting the maximum likelihood formulation of the Kalman Filter, and thereby represent all measurement models as residuals. Posing the problem in this form allows for the straightforward integration of any number of (nonlinear) constraints between two subsequent states. To solve the optimization we present a closed form recursive set of equations that directly marginalizes out information that is not required, this leads to an efficient and generic implementation. The new algorithm is applied to parameter estimation on MAVs which have two dynamic models, the MAV dynamic model and the IMU-driven model. We show the benefits and limitations of the new filtering approach on a simplified simulation example and on a real MAV system.

Authors

• Michael Burri
• Michael Bloesch
• Dominik Schindler
• Igor Gilitschenski
• Zachary Taylor
• Roland Siegwart

Keywords

• Mathematical model
• Computational modeling
• Parameter estimation
• Maximum likelihood estimation
• Sensors
• Optimization
• Parameter Estimates
• Kalman Filter
• Micro Air Vehicles
• Maximum Likelihood
• Dynamic Model
• Process Model
• Measurement Model
• Set Of Equations
• Simple Example
• Simulation Example
• Simple Simulation
• Model System
• Time Step
• Center Of Mass
• White Noise
• Gaussian Noise
• Additive Noise
• Tracking System
• Motor Speed
• Prediction Step
• Position Measurements
• Unscented Kalman Filter
• Implicit Model
• Aerodynamic Coefficients
• Moment Coefficient
• Accelerometer Measurements
• Fisher Information
• Odometry
• Process Noise